Understanding Food Price Inflation in Trinidad and Tobago: Impact of Global Shocks and the Power of Support Vector Regression Analysis

In November 2022, the retail price index for the food subcategory in Trinidad and Tobago (T&T) stood at 145, indicating a 45% rise in food prices since the base year of January 2015. A closer look at the trend reveals that food prices were relatively stable from January 2015 through September 2020. However, since then, there has been a noticeable increase in food prices, driven largely by changing external macroeconomic factors. T&T, being a small open economy in the Caribbean, is highly susceptible to such shocks.

The COVID-19 pandemic, declared by the World Health Organization in March 2020, led to global lockdowns that disrupted supply and demand. The gradual easing of restrictions fueled a rebound in demand but supply chains struggled to keep pace, resulting in inflationary pressures on food prices. Additional factors include the 2021 Suez Canal blockage, global shipping container shortages, and port congestions at key U.S. ports, which further inflated freight costs. Extreme weather events such as droughts in Brazil, wildfires in the U.S., crop failures in China, and floods in Germany also contributed to rising food prices globally. The outbreak of bird flu in 2022 in the U.S. pushed prices up for poultry and eggs. Furthermore, the Russia-Ukraine conflict beginning in February 2022 disrupted Black Sea grain exports—important for wheat and corn supplies worldwide—and sparked concerns about global food security, thus influencing food price inflation even in T&T.

Despite these developments, empirical research examining the direct relationship between these global shocks, freight rates, and food inflation in T&T has been limited. In 2022, Dr. Don Charles authored a key study titled "The Lead-Lag Relationship Between International Food Prices, Freight Rates, and Trinidad and Tobago’s Food Inflation: A Support Vector Regression Analysis." This research employed Support Vector Regression (SVR); a powerful machine learning technique suited for capturing complex, nonlinear relationships; to investigate the degree to which shipping costs and international food prices predict food inflation in T&T, as well as the specific impacts of the Russia-Ukraine conflict.

Unlike traditional regression models, SVR employs support vectors to model the underlying relationship while minimizing generalization error, making it particularly effective for economic data with nonlinear patterns. Dr. Charles divided data into training and testing sets to validate the model's robustness and accuracy.

If you require cutting-edge economic research driven by machine learning and data science methodologies capable of modeling complex, nonlinear phenomena in trade, inflation, finance, or macroeconomics, look no further. At Impulse Response Enterprises Ltd, we specialize in developing advanced machine learning models like Support Vector Regression tailored to deliver actionable insights across economic domains.

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